Excellent 👍
Now we start the REAL AWS learning — this is where most people fail, and where you’ll stand out.

🔐 LESSON 2 — AWS IAM for Python & PySpark Developers (HANDS-ON)

🎯 Lesson Outcome

By the end of this lesson, you will:

• ❌ Stop using root user
• ✅ Create an IAM user
• ✅ Create an IAM role (EC2 → S3)
• ✅ Understand IAM policies (JSON)
• ✅ Be interview-ready on IAM

🧠 WHY IAM IS CRITICAL (1-Minute Reality Check)

If you don’t know IAM well:

• ❌ EMR jobs fail
• ❌ Glue can’t read S3
• ❌ Lambda can’t access data
• ❌ Interview rejection

📌 IAM = security + permissions + identity

🧩 IAM Mental Model (Remember Forever)

IAM
 ├── User     → Human (YOU)
 ├── Role     → AWS Service (EC2, EMR, Lambda)
 ├── Policy   → Permission Rules (JSON)

🧠 Golden Rule:

Humans use Users, services use Roles

🧱 IAM Components (Visual)

1️⃣ Step 1 — Create IAM User (Stop Using Root)

🔹 Go to:

AWS Console → IAM → Users → Create user

🔹 User Details

• User name:
  👉 rajeev-data-engineer
• Access type:
  • ✅ AWS Management Console
  • ❌ Programmatic (we’ll add later)

🔹 Permissions

Choose:

• Attach policies directly
• Select:
  • AdministratorAccess (temporary for learning)

📌 Later we’ll restrict permissions (best practice)

🔐 Step 2 — Login as IAM User

1. Sign out (root)
2. Use IAM user login URL
3. Login as rajeev-data-engineer

👉 From now on, never use root

🧪 TASK 1 (Reply Required)

Confirm:

IAM user created: YES
Logged in as IAM user: YES

2️⃣ Step 3 — IAM Policies (UNDERSTAND JSON)

Example: S3 Read Policy

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": ["s3:GetObject", "s3:ListBucket"],
      "Resource": "*"
    }
  ]
}

🧠 Breakdown:

• Effect → Allow / Deny
• Action → What you can do
• Resource → On which AWS resource

📌 Interviews LOVE this

3️⃣ Step 4 — IAM Role (MOST IMPORTANT FOR PYSPARK)

🧠 Why Roles?

Because:

• EC2 / EMR should access S3
• ❌ Without hard-coded credentials

🔹 Create Role

IAM → Roles → Create role

• Trusted entity: AWS service
• Use case: EC2
• Attach policy:
  • AmazonS3FullAccess (temporary)

Name:

EMR-S3-Access-Role

📌 This role will be attached to:

• EC2
• EMR
• Glue

🧠 On-Prem vs AWS Mapping (IMPORTANT)

🧪 TASK 2 (Reply Required)

Confirm:

IAM Role created: YES
Role name:

💡 Interview Questions You Can Now Answer

✔ Difference between IAM User and Role
✔ Why roles are preferred over access keys
✔ How EMR accesses S3 securely
✔ What is least privilege access

🧾 Resume Bullet You Earn

Implemented IAM users, roles, and policies for secure access between AWS services (EC2, S3, EMR)

⏭ NEXT LESSON (VERY EXCITING)

📦 LESSON 3 — Amazon S3 for Data Engineers (HDFS → S3)

You will:

• Create S3 buckets
• Design raw / cleansed / curated
• Upload data
• Access S3 from Python
• Understand partitioning & cost

Amazon S3 will become your new HDFS

⚠️ Before Moving On — One Quick Question

👉 Are you using Asia Pacific (Mumbai) region right now?

Reply with:

Region confirmed: YES / NO

Once confirmed, we jump straight into Lesson 3 (S3 Hands-on) 🚀